Temperature-density distribution

Month-wise change in temperature

We can see that the temperature has been steadily rising across years.

climate_change <- read.csv(file = "Weather Data Bangladesh (1948 - 2013).csv",TRUE, sep = ",", stringsAsFactors = FALSE)

library(ggplot2)
library(plotly)

Min_t2 <- ggplot(climate_change, aes(as.factor(Month), Min.Temp)) + 
  geom_point(aes(color = as.factor(YEAR))) + 
  geom_line(aes(group = as.factor(YEAR), 
                color = as.factor(YEAR)), 
            alpha = 0.7) + 
  labs(title = 'Minimum Temperature by month') +
  xlab("Months") +
  ylab("Temperature") +
  theme(axis.text.x = element_text(size = 6,angle = 90,hjust = 0.5, vjust = 0.5))
# theme(legend.position = "none")
ggplotly(Min_t2)

Max_t2 <- ggplot(climate_change, aes(as.factor(Month), Max.Temp)) + 
  geom_point(aes(color = as.factor(YEAR))) + 
  geom_line(aes(group = as.factor(YEAR), 
                color = as.factor(YEAR)), 
            alpha = 0.7) + 
  labs(title = 'Maximum Temperature by month') +
  xlab("Months") +
  ylab("Temperature") +
  theme(axis.text.x = element_text(size = 6,angle = 90,hjust = 0.5, vjust = 0.5))
# theme(legend.position = "none")
ggplotly(Max_t2)

knitr::opts_chunk$set(echo = TRUE)

Temperature-density distribution

As previous plot was a little bit crowded, ‘Temperature-density’ distribution plot was created. From the Temperature-density distribution, we can see how the minimum temperature has increase suggesting a higher overall temperature aiding to increased evaporation rate and chances of heavy rain.

library(ggridges)

Min_t3 <- ggplot(climate_change, aes(x = Min.Temp, y = as.factor(YEAR))) + 
  geom_density_ridges_gradient(aes(fill = ..x..), 
                               scale = 3, size = 0.3, alpha = 0.5) +
  scale_fill_gradientn(colours = c("#0D0887FF", "#CC4678FF", "#F0F921FF"),
                       name = "Temp") +
  labs(title = 'Minimum Temperature density') + 
  theme(legend.position = c(0.9,0.2)) +
  xlab("Temperature") + 
  ylab("Year")+theme_minimal(base_size = 10)

plot(Min_t3)

Max_t3 <- ggplot(climate_change, aes(x = Max.Temp, y = as.factor(YEAR))) + 
  geom_density_ridges_gradient(aes(fill = ..x..), 
                               scale = 3, size = 0.3, alpha = 0.5) +
  scale_fill_gradientn(colours = c("#0D0887FF", "#CC4678FF", "#F0F921FF"),
                       name = "Temp") +
  labs(title = 'Maximum Temperature density') + 
  theme(legend.position = c(0.9,0.2)) +
  xlab("Temperature") + 
  ylab("Year")+theme_minimal(base_size = 10)

plot(Max_t3)

knitr::opts_chunk$set(echo = TRUE)

Changing temperature trends

From the changing temperature trends, it can be observed that most of the hottest years were in last few decades. The minimum temperature also did not go below 10 degree Celsius a lot during this time.

Min_t4 <- ggplot(data = climate_change, 
       mapping = aes(x =Min.Temp,
                     xend=Min.Temp,
                     y = reorder(YEAR,Min.Temp))) + geom_dotplot( 
                       aes(fill = ..x..)) +  scale_fill_gradient(
                         low = "yellow",high = "red") +
  labs(title = 'Changing Temperature Trends')

plot(Min_t4)

knitr::opts_chunk$set(echo = TRUE)